Machine Learning in Insurance
Announcements from the Swiss Association of Actuaries organiser: We are proud to announce that the 2020 International Summer School of the Swiss Association of the Actuaries will be held in Lausanne from 10 to 14 August 2020.
The artificial intelligence (AI) industry has been growing rapidly over the past few years, to such an extent that some now class it as an industry, no longer “emerging” but clearly “emerged.” As we enter 2020, it seems like the perfect opportunity to assess the industry as a whole and in particular how it affects the design and management of insurance products.
At the heart of these developments are a number of basic data science and machine learning modelling ‘techniques and tools’ that actuaries need to understand in order to keep up to date. Big data, predictive models and automated decision making have disrupted the insurance new business, underwriting and risk management processes over the past few years. How did we get here? What does the future hold?
For ticket prices and to register, please click here: http://saa-iss.ch/registration/
You must register by 31st of May.
This year’s International Summer School of the Swiss Association of Actuaries (ISS2020) will review the lessons learned from the evolutionary process and discuss what the potential next steps are in this journey. We will address for instance the following questions: “Are real-time fully underwritten decisions an achievable goal?” and “How can risk management decisions be learned automatically through machine learning?”
We will start from ground zero, reviewing basic techniques already included in classical data science textbooks such as “An Introduction to Statistical Learning; with Applications in R”, by G. James, D. Witten, T. Hastie and R. Tibshirani, published by Springer in 2013. As no single textbook addresses fully the insurance applications of these techniques, over the course of a week we will cover a selection of the most important machine learning methods for actuaries. The second portion of the course will be dedicated to life insurance risk models and their inclusion in hedging procedures for life insurance products tied to investment returns. Traditional hedging procedures shall be reviewed, followed by an introduction to reinforcement learning which allows learning optimal hedging policies from experience. No prerequisites are required other than an intermediate level knowledge of Statistics and the use of databases as well as statistical software.
The emphasis will be on insurance applications, both in life and non-life, and learning through examples. The teacher will be joined by Alexandre Carbonneau who will help with the insurance illustrations, exercises and illustrative scripts in the open-source R language.
José Garrido:
Dr. José Garrido is a Full Professor at the Department of Mathematics and Statistics at Concordia University, in Montreal, Canada.
After working as an actuarial analyst for Towers Watson in Montreal, Prof. Garrido received a Masters from Université Catholique de Louvain, in Belgium, and his PhD in 1987 from the Department of Statistics and Actuarial Sciences at the University of Waterloo, Canada. He is an Associate of the Society of Actuaries and of the Canadian Institute of Actuaries (CIA). His research interests are in Risk Theory, Loss Models, Insurance Statistics, Credibility Theory, Risk Management and Credit Risk, Machine Learning in Insurance, Predictive Modelling and Robust Statistics.
Prof. Garrido has written more than 50 articles in international refereed journals and conference proceedings. He is Associate Editor of several journals, including Insurance: Mathematics and Economics and the North American Actuarial Journal, as well as an Editor of the European Actuarial Journal and of the open access journal Risks. Prof. Garrido is a past President of the Actuarial Section of the Statistical Society of Canada, current Chair of the Academic Research Committee of the CIA and has served on the Scientific Committee of numerous international actuarial conferences. He is active in graduate education, having supervised 40 MSc, 14 PhD and 8 post-doctoral students.
Frédéric Godin:
Frédéric Godin is an Assistant Professor at the Mathematics and Statistics Department of Concordia University in Montreal, Quebec, Canada. His research interests are financial engineering, risk management, actuarial science, stochastic modeling, dynamics programing, variable annuities and energy markets. Frédéric holds the Fellow of the Society of Actuaries (FSA) and Associate of the Canadian Institute of Actuaries (ACIA) designations. He is part of the Quantact research group and a member of the Centre de recherches mathématiques (CRM). Before joining Concordia University, Frédéric performed numerous consulting mandates in financial risk management for large financial institutions such as banks, insurance companies and asset management firms.